Managing Fleets of LEO Satellites: Nonlinear, Optimal, Efficient, Scalable, Usable, and Robust

Abstract

Size and weight limitations of low-earth orbit (LEO) small satellites make their operation rest on a fine balance between solar power infeed and power demands of communication technologies on board, buffered by on-board battery storage. As a result, the problem of planning battery-powered payload utilization together with intersatellite communication is extremely intricate. Nevertheless, there is a growing trend toward constellations and megaconstellations that are to be managed using sophisticated software support. Earlier work has leveraged cost-optimal reachability in priced timed automata for deriving near-optimal finite-horizon schedules to operate a single LEO satellite in orbit. This article harvests that work and improves it in several dimensions, all needed for true in-orbit applicability: 1) the battery representation is no longer bound to be linear, but can be kinetic, which means that the optimization problem includes nonlinearities; 2) the management is perpetuated by a receding horizon scheduling strategy; 3) the model is continuously improved with the latest telemetry received from orbit; 4) a tandem of satellites equipped with state-of-the-art intersatellite link transponders is considered; 5) the core optimization problem is now solved using dynamic programming with antichain-based pruning, which is proven to be optimal and despite all the additional features outperforms the earlier approach by orders of magnitude; 6) the entire approach is grounded in the concrete requirements of the GOM X-4 LEO mission; 7) care is taken to make the approach usable by the space engineers, and robust against failures of parts of the toolchain; and 8) an extensive test campaign validates accuracy, efficiency, scalability, and robustness with respect to the operational requirements and constraints of LEO constellations.